Final Report
Alison Cheek & Elizabeth Bekele
2022-05-06
We were excited to do our report over this data because it was relatively tidy and had quite a few categorical variables and options for additional columns to graph.
Introduction
- Air pollution can be detrimental to both our health and the climate
- Outdoor and indoor air pollution cause chronic pain, respiratory diseases, shortened lifespan
- Air pollution kills about 7 million people worldwide every year
- Hopefully this information can showcase the importance of air pollution and that we should be more mindful about our planet
- Overview
- We will see how different air pollution types affect the population
- compare past and present population numbers
- determine which air pollutant type has the highest associated death rate
Packages Required
#This will allow us to filter through our data
library(tidyverse)
library(dplyr)
#This will help us plot figures to showcase our findings
library(ggplot2)
#This will help us organize and display our data as necessary
library(knitr)
library(kableExtra)
#This expands our plot uses
library(plotly)
#Scientific Notation Disabled
options(scipen=999)Deaths Data
- Deaths from pollution:
- Kaggle: [https://www.kaggle.com/datasets/akshat0giri/death-due-to-air-pollution-19902017]
- Author: Akshat Giri
- Last update: 2 years ago
Import the deaths-due-to-air-pollution data
deaths_df_old <- data.frame(read.csv("death-rates-from-air-pollution.csv"))
glimpse(deaths_df_old)## Rows: 6,468
## Columns: 7
## $ Entity <chr> "Afghanistan", "Afghan…
## $ Code <chr> "AFG", "AFG", "AFG", "…
## $ Year <int> 1990, 1991, 1992, 1993…
## $ Air.pollution..total...deaths.per.100.000. <dbl> 299.4773, 291.2780, 27…
## $ Indoor.air.pollution..deaths.per.100.000. <dbl> 250.3629, 242.5751, 23…
## $ Outdoor.particulate.matter..deaths.per.100.000. <dbl> 46.44659, 46.03384, 44…
## $ Outdoor.ozone.pollution..deaths.per.100.000. <dbl> 5.616442, 5.603960, 5.…Fixed: use rename instead of colnames
We are going to rename a few of the columns and glimpse the data
deaths_df<- deaths_df_old %>% rename(country=Entity, acronym=Code, year=Year, total_deaths=Air.pollution..total...deaths.per.100.000., indoor_deaths=Indoor.air.pollution..deaths.per.100.000., outdoor_deaths=Outdoor.particulate.matter..deaths.per.100.000., ozone_deaths=Outdoor.ozone.pollution..deaths.per.100.000.)
glimpse(deaths_df)## Rows: 6,468
## Columns: 7
## $ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanist…
## $ acronym <chr> "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG",…
## $ year <int> 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1…
## $ total_deaths <dbl> 299.4773, 291.2780, 278.9631, 278.7908, 287.1629, 288.0…
## $ indoor_deaths <dbl> 250.3629, 242.5751, 232.0439, 231.6481, 238.8372, 239.9…
## $ outdoor_deaths <dbl> 46.44659, 46.03384, 44.24377, 44.44015, 45.59433, 45.36…
## $ ozone_deaths <dbl> 5.616442, 5.603960, 5.611822, 5.655266, 5.718922, 5.739…Data Variables
Variables that interest us here include:
- country
- total_deaths: per 100,000
- indoor_deaths: Indoor air pollution is considered
pollution that occurs in the household. Cooking with solid fuels:
- Wood
- Crop waste, dung
- Charcoal, coal
- outdoor_deaths: Outdoor air or ambient air are
emissions caused by combustion processes from motor vehicles, solid fuel
burning and industry
- Ozone (O3)
- Particulate matter (PM10 and PM2.5)
- Nitrogen dioxide (NO2)
- Carbon monoxide (CO)
- Sulfur dioxide (SO2)
- ozone_deaths: Ozone is a gas that occurs both in
Earth’s upper atmosphere and at ground level. Ozone in the atmosphere is
an important and helpful greenhouse gas, but ground-level ozone is
created by extensive use of fossil fuels:
- Pollutants emitted by cars
- Power plants, industrial boilers, refineries, chemical plants
World Population Data
- World Population:
- Kaggle: [https://www.kaggle.com/datasets/imdevskp/world-population-19602018]
- Author: Devakumar K. P.
- Last update: 2 Years ago
Now, let’s take a look at the population data.
world_pop <- read.csv("population_total_long.csv")
glimpse(world_pop)## Rows: 12,595
## Columns: 3
## $ Country.Name <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorra", "…
## $ Year <int> 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 196…
## $ Count <int> 54211, 8996973, 5454933, 1608800, 13411, 92418, 20481779,…To get a general idea of ‘deaths-dataframe’ we made, let’s make a plots to see what’s happening. This is a plot of indoor x outdoor deaths around the world by country.
This is a mess, and so we chose two countries from each continent (a high-population and a low-population country) to graph.
We selected a high population from each continent and used the formula below to determine the low population.
Low population = high population * .10
- We wanted to have a consistent variation between our selection of low and high populated countries.
- Based on our calculation we selected the low populated country that was closest to 10% of the high populated country.
- EX: U.S (high populated) = 331002651. If we multiply by .10 we get 33100265.1 and Canada (low populated) was a population of 37742154 which was closest to 10% of the US population in 2020
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Combine Data Sets
First let’s look at a table of the high and low populated countries using the world population data set.
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Next, we are going to see the death count for high and low populated countries using the deaths dataframe.
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Lastly, we will join the population and and deaths with its respected country.
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This is a closer view on the population growth over time in both the high and low populated countries that we selected.
Death Count
Which country has the highest average death count?
Let’s make a table depicting the high and low populated countries and their respected death count due to pollution.
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Here’s a graph to clearly visualize the previous table
So we’ve looked at the deaths due to pollution, but what percentage of the population was affected?
In order to get rid of the leading zeros, and clean up the y-axis, we multiplied the ‘percent_high’ and ‘percent_low’ calculation by 10,000,000
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Pollution Types
Which type of pollution has the greatest number of deaths?
| country | avg_indoor | avg_outdoor | avg_ozone |
|---|---|---|---|
| Pakistan | 87.7427944 | 50.52063 | 10.440656 |
| Nigeria | 75.8755074 | 35.21678 | 2.117076 |
| Brazil | 19.4258385 | 26.84194 | 2.740342 |
| Germany | 0.7170881 | 25.47078 | 2.343892 |
| Australia | 0.2485867 | 17.20789 | 0.360452 |
| United States | 0.1656402 | 22.79947 | 3.915093 |
| country | avg_indoor | avg_outdoor | avg_ozone |
|---|---|---|---|
| Canada | 0.0651156 | 16.38423 | 1.9697041 |
| Chile | 8.6932699 | 27.17442 | 0.8504919 |
| Malawi | 132.1891749 | 13.81151 | 3.3870514 |
| New Zealand | 0.2908622 | 15.56872 | 0.0727512 |
| Serbia | 35.8762796 | 42.71254 | 2.9395671 |
| Sri Lanka | 44.5428441 | 24.77233 | 0.4304406 |
| country | avg_indoor | avg_outdoor | avg_ozone |
|---|---|---|---|
| Canada | 0.0651156 | 16.38423 | 1.9697041 |
| Chile | 8.6932699 | 27.17442 | 0.8504919 |
| Malawi | 132.1891749 | 13.81151 | 3.3870514 |
| New Zealand | 0.2908622 | 15.56872 | 0.0727512 |
| Serbia | 35.8762796 | 42.71254 | 2.9395671 |
| Sri Lanka | 44.5428441 | 24.77233 | 0.4304406 |
Pollution Over Time
Let’s look at the previous two decades and compare the death count
This is the first decade 1996-2006has there been a change?
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This is the second decade 2007-2017
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Let’s graph the previous tables!
The first decade 1996-2006.
This shows the second decade 2007-2017.
By comparing each pollutant type, we can determine which year and country had the highest numbers of deaths
Indoor Deaths
Outdoor Deaths
Ozone Deaths
Which is worse?
outdoor or indoor pollution?
Let’s reintroduce a graph we looked at earlier. Instead this time we will combine the pollutant types together.
We cannot conclude which is worse.
- High Populated Countries:
- Outdoor pollution seems to be more detrimental with the exception of two countries in this sample set.
- Low Populated Countries:
- it’s half-and-half
Summary
- Which country has the highest average death count?
- High Population: Pakistan
- Low Population: Malawi
- Has the percentage of the affected population decreased or increased
over time?
- Generally it is decreasing for both High and Low populated countries
- Which pollutant type has the greatest number of deaths?
- High Population: Indoor Pollution
- Low Population: Indoor Pollution
- How has the death count changed over the past two decades?
- 1996-2006:
- High Population: Decreases
- Low Population: Decreases
- 2007-2017:
- High Population: Decreases
- Low Population: Decreases
- 1996-2006:
- Which year and country had the highest number of deaths per
pollutant type?
- We looked at years 1996-2017
- Indoor: Pakistan and Malawi were mainly affected in 1996
- Outdoor: Serbia and Pakistan were the top countries.
- 2011 was the worst for Pakistan.
- 1997 was the worst for Serbia
- Sri Lanka and Tonga increased, but Sri Lanka had a steep decrease after 2015
- Outdoor Ozone: Pakistan and Malawi were the top countries.
- 1997 was the worst for Pakistan.
- 1998 was the worst for Malawi
- United States was the second highest amount of deaths among the higher populated countries
- Pakistan decreased and then slightly increased
- Which is worse - outdoor or indoor pollution?
- Inconclusive
Sources
[https://www.kaggle.com/datasets/akshat0giri/death-due-to-air-pollution-19902017 ]
[https://www.epa.gov/ground-level-ozone-pollution/ground-level-ozone-basics]
[https://www.health.nsw.gov.au/environment/air/Pages/outdoor-air-pollution.aspx]
[https://www.kaggle.com/datasets/imdevskp/world-population-19602018]